Quantum Machine Learning for Healthcare Data Processing

The advent of quantum computing holds great potential for enhancing the computational efficiency of machine learning algorithms through quantum machine learning theories and algorithms. Although the deployment of quantum computers is still in an early development stage, we can get access to many cloud-based services that provide accessible quantum computing environments to run quantum machine learning applications, such as IBM Quantum Experience, Google Quantum AI platform, and Microsoft Quantum Development Kit. Besides, we can conduct a classical simulation of quantum machine learning using quantum programming toolkits like Pennylane and Torch Quantum on classical CPU/GPU platforms.

Quantum machine learning (QML) refers to an emerging interdisciplinary field that integrates the principles of quantum mechanics and machine learning. With rapid advances in quantum computing, we have witnessed the noisy intermediate-scale quantum (NISQ) era that admits as many as a few hundred qubits available for implementing many QML algorithms on real quantum computers to enhance the efficiency and speed of machine learning applications. Despite the limitations of the NISQ era, which is characterized by a small number of qubits and high levels of quantum noise, we exploring the potential of quantum computing for machine learning.

This proposal focuses on the use of quantum machine learning for healthcare data processing, which supports a diverse study field including several applications, i.e., medical specifications and related diseases. Some of these diseases are well-known and mastered by physicians, while others are not. Healthcare practitioner's technical and scientific advancements with healthcare data have become increasingly diverse, including a wide variety of clinical analyses and metrics, biological parameters, and medical imaging modalities. Healthcare data are generally asymmetrical, non-stationary, and classified by a high degree of sophistication due to the volume of data and the completeness of some unusual conditions. Thus, the deployment of quantum machine learning can benefit many physicians and patients in healthcare domains, such as medical imaging, clinical diagnostics, e-healthcare Records, and associated diseases.

To emphasize the potential advantages of quantum machine learning for healthcare data processing, in this proposal, we concentrate on three typical healthcare applications: (1) Leveraging quantum generative neural networks and quantum variational eigensolver for the application of drug discovery; (2) Building up a quantum kernel learning (QKL) and quantum-classical hybrid neural networks to better represent multi-model features of biomedical data like Schizophrenia and Schizo-affective Disorder; (3) Setting up a quantum federated learning architecture for concerning privacy-preserving of healthcare data.